CN112286206A

CN112286206A - Automatic driving simulation method, system, equipment, readable storage medium and platform

Info

Publication number: CN112286206A
Application number: CN202011286050.2A
Authority: CN
Inventors: 韩坪良; 商伯涵; 王维
Original assignee: Suzhou Zhijia Technology Co Ltd
Current assignee: Suzhou Zhijia Technology Co Ltd
Priority date: 2020-11-17
Filing date: 2020-11-17
Publication date: 2021-01-29
Anticipated expiration: 2040-11-17
Also published as: CN112286206B; WO2022105394A1

Abstract

The application discloses an automatic driving simulation method, system, equipment, readable storage medium and platform, and relates to the field of simulated driving. The method comprises the following steps: acquiring a reference driving data set; operating a simulated driving platform according to the reference lane data; acquiring simulated vehicle attitude data of a simulated vehicle in real time; and determining the position deviation between the simulated vehicle attitude data and the reference vehicle attitude data, and planning the driving track of the simulated vehicle according to the position deviation and the corresponding relation between the reference traffic data and the reference vehicle attitude data to obtain the simulated driving closed-loop simulation data. The automatic driving process of the simulated vehicle is subjected to closed-loop simulation by acquiring the reference driving data set, and the driving track plan of the simulated vehicle is determined in real time according to the reference lane data and the reference traffic data, so that the accuracy of the simulated driving test is improved, and the authenticity of the simulated driving test is improved.

Description

Automatic driving simulation method, system, equipment, readable storage medium and platform

Technical Field

The embodiment of the application relates to the field of simulated driving, in particular to an automatic driving simulation method, system, equipment, readable storage medium and platform.

Background

Automatic driving is a technique for causing a vehicle to automatically travel without a human driver. Prior to market release, extensive testing of the autopilot system was required to ensure the safety and reliability of the system. It is generally recognized that an autopilot system requires at least 110 billion miles of testing mileage to meet safety and reliability requirements.

The above test can be done by an actual road test. However, actual road testing often has difficulty providing the required test volumes. Furthermore, actual road testing is often limited by actual road conditions, making it difficult to test an autonomous driving system for special scenarios.

The autopilot system may also be tested on the simulation platform based on the constructed simulated driving scenario and by simulating simulated driving of the autopilot vehicle on the simulation platform. The test of the automatic driving system under the extreme driving condition can be completed by simulating a scene environment which can occur, particularly simulating some special scenes such as dangerous scenes and extreme scenes. The simulation platform can also perform testing by using actual road driving data collected from the real world, but the problem of scene selection error or scene loss is easy to occur due to the fact that the actual road driving data needs to be converted into related scenes. In addition, the cost of collecting and labeling actual road driving data is also quite high.

What is needed is a method that addresses at least one or more of the problems set forth above.

Disclosure of Invention

The embodiment of the application provides an automatic driving simulation method, system, equipment, readable storage medium and platform. The embodiment of the application can at least improve the testing efficiency of the automatic driving algorithm, for example, at least improve the accuracy of the testing result of the automatic driving algorithm, reduce the time required for completing the test or reduce the quantity of the test, and the like, thereby improving the overall reliability and safety of the automatic driving system.

In one aspect, a method for simulating automatic driving is provided, the method comprising:

acquiring a reference driving data set, wherein the reference driving data set comprises a reference lane data subset, a reference traffic data subset and a reference vehicle posture data subset, and reference lane data in the reference lane data subset, reference traffic data in the reference traffic data subset and reference vehicle posture data in the reference vehicle posture data subset have a corresponding relation;

operating a simulation driving platform according to the reference lane data so that a simulation vehicle can automatically simulate driving on the simulation driving platform;

acquiring simulated vehicle attitude data of the simulated vehicle in real time;

determining a position deviation between the simulated vehicle posture data and the reference vehicle posture data, and determining target traffic data corresponding to the simulated vehicle posture data from the reference traffic data according to the position deviation and a corresponding relation between the reference traffic data and the reference vehicle posture data, wherein the position deviation is used for indicating a geographical position difference of the simulated vehicle and a reference vehicle corresponding to the reference driving data set on a road;

planning the driving track of the simulated vehicle according to the reference lane data and the target traffic data to obtain simulated driving closed loop simulation data.

In an alternative embodiment, the reference traffic data includes position data of road participating vehicles;

the planning of the driving track of the simulated vehicle according to the reference lane data and the target traffic data to obtain simulated driving closed-loop simulation data comprises:

according to the reference lane data and the position data of the road participating vehicles, carrying out intention track prediction on the road participating vehicles to obtain prediction data of the participating vehicles;

planning the driving track of the simulated vehicle according to the reference lane data and the predicted data of the participating vehicles to obtain track planning data;

and determining the simulation data of the simulated driving closed loop according to the trajectory planning data.

In an optional embodiment, the determining the simulated driving closed loop simulation data according to the trajectory planning data includes:

receiving feedback data returned by the simulated vehicle, wherein the feedback data is used for indicating the simulated driving condition of the simulated vehicle;

generating a control command according to the feedback data and the trajectory planning data;

operating the simulated vehicle according to the control command, and generating updated simulated vehicle posture data according to the operation result of the simulated vehicle;

and according to the updated simulated vehicle attitude data, repeatedly executing the step of determining the position deviation between the simulated vehicle attitude data and the reference vehicle attitude data, and according to the position deviation and the corresponding relation between the reference traffic data and the reference vehicle attitude data, determining target traffic data corresponding to the simulated vehicle attitude data from the reference traffic data to obtain the simulated driving closed loop simulation data.

In an optional embodiment, after obtaining the simulated driving closed loop simulation data, the method further includes:

obtaining a test key index from the simulation driving closed loop simulation data;

and evaluating the test key indexes to obtain a platform evaluation result of the simulated driving platform.

In an optional embodiment, the evaluating the test key indicator to obtain a platform evaluation result of the driving simulation platform includes:

obtaining an index evaluation result from the test key index;

and responding to the failure of the index evaluation result, acquiring the time positioning of the failed index evaluation result in the automatic driving process, and obtaining the platform evaluation result.

In an optional embodiment, the test key indexes comprise at least one of a brake index, an acceleration index and a vehicle distance index;

the brake index corresponds to the brake acceleration of the simulated vehicle;

the acceleration index corresponds to the acceleration of the simulated vehicle;

the vehicle distance index corresponds to the distance between the simulated vehicle and other simulated vehicles on the road in the appointed driving stage.

In an optional embodiment, the method further comprises:

in response to the braking acceleration reaching a first acceleration requirement, determining that the braking indicator fails;

in response to the acceleration rate reaching a second acceleration request, determining that the acceleration index fails;

determining that the inter-vehicle distance indicator fails in response to the simulated vehicle being less than a distance threshold from other simulated vehicles of the road during the designated driving phase.

In an alternative embodiment, the acquiring the reference driving data set includes:

acquiring a data packet list, wherein the data packet list comprises data packets respectively corresponding to different driving time periods, and the data packets are arranged in a forward direction according to the driving time periods;

and sequentially reading the reference driving data set in the data packet from the data packet list.

In an optional embodiment, the data packet includes the reference driving data set corresponding to an image frame in a reference driving video;

the sequentially reading the reference driving data set in the data packet from the data packet list comprises:

sequentially reading the data packets from the data list;

and acquiring the reference driving data corresponding to the image frames from the data packet frame by frame and caching the reference driving data.

In another aspect, there is provided an automated driving simulation system, the system comprising:

the system comprises an acquisition module, a processing module and a display module, wherein the acquisition module is used for acquiring a reference driving data set, and the reference driving data set comprises a reference lane data subset, a reference traffic data subset and a reference vehicle posture data subset, wherein the reference lane data in the reference lane data subset, the reference traffic data in the reference traffic data subset and the reference vehicle posture data in the reference vehicle posture data subset have corresponding relations;

the operation module is used for operating a simulation driving platform according to the reference lane data so that a simulation vehicle can automatically simulate driving on the simulation driving platform;

the acquisition module is also used for acquiring the simulated vehicle attitude data of the simulated vehicle in real time;

a determining module, configured to determine a position deviation between the simulated vehicle posture data and the reference vehicle posture data, and determine target traffic data corresponding to the simulated vehicle posture data from the reference traffic data according to the position deviation and a corresponding relationship between the reference traffic data and the reference vehicle posture data, where the position deviation is used to indicate a difference in geographic positions of the simulated vehicle and a reference vehicle corresponding to the reference driving data set on a road;

and the planning module is used for planning the driving track of the simulated vehicle according to the reference lane data and the target traffic data to obtain simulated driving closed loop simulation data.

the planning module comprises:

the prediction unit is used for predicting the intention track of the road participation vehicle according to the reference lane data and the position data of the road participation vehicle to obtain the prediction data of the road participation vehicle;

the planning unit is used for planning the driving track of the simulated vehicle according to the reference lane data and the predicted data of the participated vehicle to obtain track planning data;

and the determining unit is used for determining the simulation data of the simulated driving closed loop according to the trajectory planning data.

In an optional embodiment, the determining unit is further configured to receive feedback data returned by the simulated vehicle, where the feedback data is used to indicate a simulated driving condition of the simulated vehicle; generating a control command according to the feedback data and the trajectory planning data;

the determining unit is further configured to operate the simulated vehicle according to the control command, and generate the updated simulated vehicle posture data according to an operation result of the simulated vehicle; and according to the updated simulated vehicle attitude data, repeatedly executing the steps of determining the position deviation between the simulated vehicle attitude data and the reference vehicle attitude data and determining target traffic data corresponding to the simulated vehicle attitude data from the reference traffic data according to the position deviation and the corresponding relation between the reference traffic data and the reference vehicle attitude data to obtain the simulated driving closed loop simulation data.

In an optional embodiment, the obtaining module is further configured to obtain a test key indicator from the simulated driving closed-loop simulation data;

the determination module is further used for evaluating the test key indexes to obtain a platform evaluation result of the driving simulation platform.

In an optional embodiment, the obtaining module is further configured to obtain an index evaluation result from the test key index; and responding to the failure of the index evaluation result, acquiring the time positioning of the failed index evaluation result in the automatic driving process, and obtaining the platform evaluation result.

In an optional embodiment, the key indicators include at least one of a braking indicator, an acceleration indicator and a vehicle distance indicator;

the brake index corresponds to the brake acceleration of the simulated vehicle;

In an optional embodiment, the determining module is further configured to determine that the braking indicator fails in response to the braking acceleration reaching a first acceleration requirement;

the determining module is further used for responding to the acceleration reaching a second acceleration requirement, and determining that the acceleration index is not passed;

the determining module is further used for responding to the fact that the distance between the simulated vehicle and other simulated vehicles on the road in the specified driving stage is smaller than a distance threshold value, and determining that the inter-vehicle distance index does not pass.

In an optional embodiment, the obtaining module is further configured to obtain a data packet list, where the data packet list includes data packets corresponding to different driving time periods, and the data packets are arranged according to the driving time periods in a forward direction; and sequentially reading the reference driving data set in the data packet from the data packet list.

In an optional embodiment, the data packet includes the reference driving data corresponding to the image frame in the reference driving video;

the acquisition module is further configured to read the data packets from the data list in sequence; and acquiring the reference driving data corresponding to the image frames from the data packet frame by frame and caching the reference driving data.

In another aspect, a computer device is provided, the computer device comprising a processor and a memory, the memory having stored therein at least one instruction, at least one program, a set of codes, or a set of instructions, which is loaded and executed by the processor to implement the method of simulating autopilot as described in any of the embodiments of the present application.

In another aspect, there is provided a computer readable storage medium having stored therein at least one instruction, at least one program, a set of codes, or a set of instructions, which is loaded and executed by a processor to implement a simulation method of autopilot as described in any of the embodiments of the present application.

In another aspect, a computer program product or computer program is provided, the computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions to cause the computer device to perform the simulation method of automatic driving as described in any of the above embodiments.

The beneficial effects brought by the technical scheme provided by the embodiment of the application at least comprise:

the automatic driving process of the simulated vehicle can be simulated in a closed loop mode based on the reference lane data, the reference traffic data and the reference vehicle posture data in the reference driving data set, the driving track plan of the simulated vehicle is determined according to the reference lane data and the reference traffic data in real time, and the testing efficiency of the automatic driving algorithm is improved.

The method and the device can at least improve the defect that an accurate simulation driving result cannot be obtained when the automatic driving is simulated according to the reference driving condition in the open-loop simulation driving test process. In the open-loop simulation driving test, since the position of the simulated vehicle needs to be continuously adjusted according to the position of the real/reference driving vehicle in the open-loop simulation driving process, a complete simulation driving process cannot be obtained. The automatic driving simulation method provided by the application improves the accuracy of the simulated driving test and improves the authenticity of the simulated driving test.

Drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.

Fig. 1 is a schematic diagram of an implementation environment of a simulation method for automatic driving according to an embodiment of the present disclosure;

FIG. 2 is a flow chart of a simulation method for autonomous driving provided by an exemplary embodiment of the present application;

FIG. 3 is a block diagram of a simulated driving system provided in an exemplary embodiment of the present application;

FIG. 4 is a flow chart of a simulation method of autonomous driving provided by another exemplary embodiment of the present application;

FIG. 5 is a block diagram of an autopilot simulation apparatus according to an exemplary embodiment of the present application;

fig. 6 is a block diagram illustrating a simulation apparatus for automatic driving according to another exemplary embodiment of the present application;

fig. 7 is a block diagram of a server according to an exemplary embodiment of the present application.

Detailed Description

To make the objects, technical solutions and advantages of the present application more clear, embodiments of the present application will be described in further detail below with reference to the accompanying drawings.

Unmanned vehicle: the unmanned vehicle is also called as an automatic vehicle and a wheeled mobile robot, and mainly achieves the purpose of unmanned driving by means of an intelligent driving instrument which is mainly a computer system and arranged in the vehicle. The unmanned vehicle is an intelligent vehicle which senses the road environment through a vehicle-mounted sensing system, automatically plans a driving route and controls the vehicle to reach a preset target. The unmanned vehicle senses the surroundings of the vehicle by using the vehicle-mounted sensor and controls the steering and speed of the vehicle according to the road, vehicle position and obstacle information obtained by sensing, thereby enabling the vehicle to safely and reliably travel on the road. The unmanned vehicle integrates a plurality of leading-edge technologies such as automatic control, a system structure, artificial intelligence, visual calculation and the like, is a product of high development of computer science, mode recognition and intelligent control technologies, is an important mark for measuring national scientific research strength and industrial level, and has wide application prospect in the fields of national defense and national economy.

Networking of vehicles: the internet of things of vehicles is characterized in that running vehicles are used as information sensing objects, network connection between the vehicles and objects such as vehicles, people, roads, service platforms and the like is achieved by means of a new-generation information communication technology, the overall intelligent driving level of the vehicles can be improved, safe, comfortable, intelligent and efficient driving feeling and traffic service are provided for users, meanwhile, traffic operation efficiency is improved, and the intelligent level of social traffic service is improved. Optionally, the vehicle-mounted device on the vehicle effectively utilizes all vehicle dynamic information in the information network platform through a wireless communication technology, and provides different functional services during vehicle operation. The internet of vehicles generally exhibits the following characteristics: the Internet of vehicles can provide guarantee for the distance between the vehicles, and the probability of collision accidents of the vehicles is reduced; the Internet of vehicles can help the vehicle owner to navigate in real time, and the efficiency of traffic operation is improved through communication with other vehicles and a network system.

Automatic driving simulation: the automatic Driving simulation technology is an application of a computer simulation technology in the field of automobiles, is more complex to develop than a traditional ADAS (Advanced Driving Assistance System) simulation System, and has very high requirements on decoupling and architecture of the System. The automatic driving simulation system carries out digital reduction and generalization on the real world in a mathematical modeling mode, and the establishment of a correct, reliable and effective simulation model (namely a path planning model) is a key and premise for ensuring that a simulation result has high reliability. The basic principle of the simulation technology is that in a simulation scene, a real controller is changed into an algorithm, and the automatic driving algorithm is tested and verified by combining the technologies of sensor simulation and the like.

Generally, in the process of automatically generating a road test simulation scene, vehicle obstacles are manually set at certain positions of a simulation scene map and information such as speed and attitude is given to generate false vehicle obstacle sensing signals, or position points of a lane line are automatically sampled in a real environment, and corresponding position points in the simulation scene map generate false lane line sensing signals, so that a real road condition scene is simulated. Alternatively, it is also possible to create a simulation scene close to the real environment based on a GPU (Graphics Processing Unit), which is similar to an animation in the real environment, and perform the calculation of the perception information based on an algorithm again.

In the process, the motion state of the vehicle barrier, the lane line and other perception information during actual drive test cannot be reflected really, noise interference is generally ignored by artificially designed information such as the vehicle barrier or the lane line, so that a simulation scene cannot better reproduce real road conditions, the simulation effect of the automatic driving simulation system is poor, an automatic driving algorithm adopted by the path planning model cannot be updated more quickly and accurately in an iterative manner, the accuracy degree of the automatic driving algorithm is influenced, and the intelligence of an automatic driving vehicle is influenced.

In view of the above, embodiments of the present application provide a simulation method for automatic driving, which can perform closed-loop simulation on an automatic driving process of a simulated vehicle based on reference lane data, reference traffic data, and reference vehicle posture data in a reference driving data set (where the reference driving data set is a data set obtained by collecting data from a driving process of a reference vehicle when the reference/real vehicle is traveling on a road, and is used for indicating driving conditions of the reference vehicle at different positions of the road, such as traffic conditions, lane conditions, vehicle posture conditions, and the like), determine a driving trajectory plan of the simulated vehicle in real time according to the reference lane data and the reference traffic data, improve testing efficiency of the automatic driving algorithm, for example, at least improve accuracy of a test result of the automatic driving algorithm, reduce time required for completing the test, reduce a quantity of the test, and the like, thereby improving the overall reliability and safety of the autopilot system. The method and the device can at least improve the defect that an accurate simulation driving result cannot be obtained when the automatic driving is simulated according to the reference driving condition in the open-loop simulation driving test process. In the open-loop simulation driving test, since the position of the simulated vehicle needs to be continuously adjusted according to the position of the real/reference driving vehicle in the open-loop simulation driving process, a complete simulation driving process cannot be obtained.

Fig. 1 is a schematic view of an implementation environment of a simulation method for automatic driving according to an embodiment of the present application. Referring to fig. 1, a vehicle 101 and a computer device 102 are included in the implementation environment.

The Vehicle 101 is configured To collect drive test data during actual driving, and optionally, the Vehicle 101 is provided with functional modules such as a Vehicle-mounted sensor, a positioning component, a camera assembly, a controller, a data processor, and an automatic driving system, where the functional modules can implement exchange and sharing of traffic participants by means of modern Mobile communication and network technologies such as a Vehicle networking, a 5G (5th Generation Mobile network, fifth Generation Mobile communication technology), and a V2X (Vehicle To X, wireless communication technology for vehicles), so as To have functions of sensing, decision planning, control execution, and the like in a complex environment.

Optionally, the vehicle 101 includes a conventional automobile, a smart car, an unmanned vehicle, an electric vehicle, a bicycle, a motorcycle, and the like, and the vehicle 101 may be operated by a driver manually or driven by an automatic driving system to realize unmanned driving.

Optionally, the vehicle-mounted sensor includes a data acquisition unit such as a laser radar, a millimeter wave radar sensor, an acceleration sensor, a gyroscope sensor, a proximity sensor, and a pressure sensor.

In some embodiments, the drive test data is a rossbag data packet returned by a ROS (Robot Operating System) during drive test of the vehicle 101, information collected by functional modules such as a camera assembly and a vehicle-mounted sensor during drive test of the vehicle 101 is stored in the rossbag data packet, and is used for sensing and tracking the position and the motion attitude of an obstacle and a lane line, optionally, Positioning data collected by a Positioning assembly based on a GPS (Global Positioning System) is also stored in the rossbag data packet, optionally, a vehicle attitude estimation of the vehicle 101 itself by an IMU (Inertial Measurement Unit, also called an Inertial Measurement instrument, or an Inertial sensor) is also stored in the rossbag data packet, and optionally, a timestamp of each kind of information is also stored in the rossbag data packet.

The carrier 101 and the computer device 102 can be directly or indirectly connected through wired or wireless communication, for example, the carrier 101 and the computer device 102 are wirelessly connected through a vehicle network, and the embodiments of the present application are not limited herein.

The computer device 102 is used to debug parameters of the simulated driving platform to iteratively update the simulated driving platform. Optionally, the computer device 102 comprises at least one of a server, a plurality of servers, a cloud computing platform, or a virtualization center. Optionally, the computer device 102 undertakes primary computing work and the carrier 101 undertakes secondary computing work; or, the computer device 102 undertakes the secondary computing work, and the carrier 101 undertakes the primary computing work; alternatively, the vehicle 101 and the computer device 102 perform cooperative computing therebetween using a distributed computing architecture.

Optionally, the carrier 101 refers to one of a plurality of carriers, and the carrier 101 has a terminal device installed thereon for performing communication connection with the computer device 102, where the type of the terminal device includes but is not limited to: at least one of a vehicle-mounted terminal, a smart phone, a tablet computer, a smart watch, a smart sound box, an electronic book reader, an MP3(Moving Picture Experts Group Audio Layer III, motion Picture Experts compression standard Audio Layer 3) player, an MP4(Moving Picture Experts Group Audio Layer IV, motion Picture Experts compression standard Audio Layer 4) player, a laptop portable computer, or a desktop computer. The terminal device is provided with an automatic driving system which can plan the driving parameters of the carrier 101 based on the path planning model debugged by the computer device 102.

Those skilled in the art will appreciate that the number of carriers 101 may be greater or less. For example, the number of the carriers 101 may be only one, or the number of the carriers 101 may be several tens or hundreds, or more. The number and the type of the carrier 101 are not limited in the embodiments of the present application.

Fig. 2 is a flowchart of a simulation method of automatic driving according to an exemplary embodiment of the present application, which is described by way of example as being applied to a computer device, and as shown in fig. 2, the method includes:

step 201, a reference driving data set is obtained, and the reference driving data set comprises a reference lane data subset, a reference traffic data subset and a reference vehicle posture data subset.

The reference driving data set may be a data set obtained from data collected from the driving process of the reference vehicle when the reference vehicle is driving on the road, and is used for indicating the driving conditions of the reference vehicle at different positions of the road, such as traffic conditions, lane conditions, vehicle posture conditions, and the like.

Wherein the reference lane data subset may include reference lane data, the reference traffic data subset may include reference traffic data, and the reference vehicle attitude data subset may include reference vehicle attitude data. The reference lane data at least comprises at least one of lane identification information, speed limit information, road material information and the like, wherein the lane identification information is information indicating that a reference vehicle is positioned in a second lane on a current road, the speed limit information indicates speed limit information corresponding to the lane where the current reference vehicle is positioned, and the road material information indicates the ground material of the vehicle where the current reference vehicle is positioned; the reference traffic data at least includes at least one of information of a road and a vehicle, traffic signal information, obstacle information and the like, wherein the information of the road participating vehicle represents information of other vehicles located on the periphery of the participating vehicle when the participating vehicle travels to a certain position, the traffic signal information represents traffic signals passed by the participating vehicle when traveling on the road, and indication conditions of the traffic signals (such as red light indicating parking, yellow light indicating slow traveling, green light indicating traveling), and the obstacle information represents obstacles appearing on the road, such as: speed bumps, road tables, and the like; the reference vehicle posture data at least comprises at least one of information such as steering wheel information, speed information, acceleration information, vehicle position information and the like, wherein the steering wheel information is used for representing the direction control condition of the current reference vehicle; the speed information is used to represent the current speed of the reference vehicle, and the acceleration information is used to represent the current acceleration of the vehicle, such as: acceleration in a starting acceleration stage or a stopping stage, and vehicle position information is used for indicating the distance between the current reference vehicle and a starting point or a terminal point. The reference lane data in the reference lane data subset, the reference traffic data in the reference traffic data subset, and the reference vehicle posture data in the reference vehicle posture data subset have a corresponding relationship.

The reference driving data set may be acquired by at least one of:

in one embodiment, the reference vehicle is a real vehicle, and the reference driving data set is obtained by collecting the reference vehicle, that is, collecting real driving data generated by the real vehicle driving on a real road. The real driving data may include vehicle attitude data, traffic data, lane data, etc. of the vehicle itself. The traffic data and the lane data may be manually input (for example, identified by a system engineer), or the traffic data and the lane data may be generated by automatically identifying a driving image generated in a driving process;

assume that a human driver drives a reference vehicle from city a and travels to the end of city B. The vehicle posture data is data of a vehicle driving situation obtained according to a driving process performed by a human driver, and includes at least one of steering wheel information, speed information, acceleration information, vehicle position information, and the like of a reference vehicle at different road positions during the driving of the reference vehicle from city a to city B. Traffic data and lane data including lane detection information, obstacle detection information, information of road-participating vehicles, and the like at different road positions during driving of a reference vehicle from city a to city B may be obtained by collecting an environmental image of the periphery of the vehicle during driving of a human driver to generate a driving video and recognizing the driving video.

Secondly, the reference vehicle is a vehicle model in a vehicle driving application program, and a player controls the vehicle model in the vehicle driving application program to generate driving data, wherein the driving data comprises vehicle posture data, traffic data, lane data and the like of the vehicle model, and the traffic data and the lane data are generated according to a three-dimensional virtual environment where the vehicle model is located, or the traffic data and the lane data are generated by automatically identifying a driving image generated in the driving process of the vehicle model;

illustratively, the vehicle model is triggered from a starting point of the three-dimensional virtual environment, a player controls the vehicle model to travel from the starting point to an end point at the terminal, vehicle posture data is generated according to control operation of the player on the terminal, wherein the vehicle posture data comprises at least one of information such as steering wheel data, speed data, acceleration data, vehicle position data and the like of the vehicle model at different positions between the starting point and the end point, and in the control process of the player on the vehicle model, the three-dimensional virtual environment around the vehicle model is subjected to image acquisition, a driving video is generated, and the driving video is identified to obtain traffic data and lane data.

In one embodiment, the reference driving data set may be obtained from real driving video of a real driving process. The real driving video is a video obtained by carrying out image acquisition on a real driving environment in the driving process of the reference vehicle. For example, data may be acquired from image frames of real driving video to form a reference driving data set. The driving video and the data in the reference driving data set have a corresponding relation.

In some embodiments, the reference lane data subset of the reference driving data set may include the following reference lane data: the reference lane data corresponds to image frames in the driving video, such as: the nth frame in the driving video corresponds to reference lane data and is used for representing the lane condition when the reference vehicle drives to the position corresponding to the nth frame image frame, and n is a positive integer.

In some embodiments, the reference driving data set includes a reference traffic data subset including reference traffic data corresponding to image frames in the driving video, such as: the nth frame in the driving video corresponds to reference traffic data used for representing the traffic condition when the reference vehicle drives to the position corresponding to the nth frame image frame.

In some embodiments, the reference driving data set includes a reference vehicle pose data subset including reference vehicle pose data corresponding to image frames in the driving video, such as: the nth frame in the driving video corresponds to reference vehicle posture data and is used for representing the vehicle posture condition of the reference vehicle when the reference vehicle drives to the position corresponding to the nth frame image frame.

That is, in combination with the relationship between each data subset in the reference driving data set and the image frame in the driving video, there is also a corresponding relationship between the reference lane data, the reference traffic data, and the reference vehicle posture data, and the corresponding relationship is used to represent a lane condition, a traffic condition, and a posture condition of the vehicle itself when the reference vehicle is driven to a certain position. When the reference vehicle drives to a certain position, one frame or a group of image frames (image frames between two adjacent key frames) are corresponding to a certain frame or a group of image frames in the driving video, so that one frame or a group of image frames exist and correspond to a group of reference lane data, reference traffic data and reference vehicle posture data in the driving data set.

In some embodiments, a data packet list is obtained, the data packet list includes data packets corresponding to different driving time periods respectively, the data packets are arranged in a forward direction according to the driving time periods, and reference driving data sets in the data packets are read from the data packet list in sequence.

Step 202, operating the simulated driving platform according to the reference lane data so that the simulated vehicle can automatically simulate driving on the simulated driving platform.

In some embodiments, the reference lane data is uploaded to a simulated driving platform, and the simulated vehicle performs simulated driving with the reference lane data as a lane during the simulated driving.

The simulated driving platform is a platform for completing path planning and behavior decision of a simulated vehicle based on actual driving data of a reference vehicle on the basis of unmanned driving and control, and usually the simulated driving platform completes the path planning and the behavior decision through a plurality of functional modules or a plurality of units in one functional module. In some embodiments, before the simulated driving platform is operated to automatically simulate the driving of the simulated vehicle, basic parameters of the simulated vehicle are also required to be input into the simulated driving platform, such as: vehicle weight, load capacity, maximum speed per hour, hundred kilometers acceleration, number of vehicle compartments, brake sensitivity, throttle sensitivity, and the like. Therefore, the driving simulation platform can carry out simulation control on the simulated vehicle.

Illustratively, the reference driving data set may comprise a plurality of sets of simulated lane data, wherein a first set of simulated lane data represents data for a simulated driving platform controlling a lane in which the simulated vehicle begins to travel. For example, the first set of simulated lane data may be data of a second lane of the four lanes, which indicates that the simulated driving platform controls the simulated vehicle to start driving in the second lane.

The operation of the simulated driving platform is a cyclic process, namely, in the initial stage, the simulated vehicle is operated to run on a lane corresponding to the reference lane data according to the reference lane data, and in the subsequent process, the data generated in the driving process and the reference driving data set from the real vehicle are acquired in real time according to the driving condition of the simulated vehicle, so that the simulated vehicle is continuously controlled.

And step 203, acquiring the simulated vehicle posture data of the simulated vehicle in real time.

The simulated vehicle posture data is used for representing the vehicle driving situation of the simulated vehicle in the simulation process of automatic driving, and includes steering wheel information, speed information, acceleration information, vehicle position information and the like of the simulated vehicle, wherein the vehicle position information is used for representing the current position of the simulated vehicle, and the vehicle position information can be represented by a distance from a starting point or coordinates in a coordinate system constructed between the starting point and an end point, and the embodiment of the application is not limited thereto.

The vehicle position information may be obtained by positioning the simulated vehicle in real time, or the vehicle position information may be estimated from speed information, acceleration information, and steering wheel information of the simulated vehicle in a previous simulated driving.

Illustratively, the current driving speed of the simulated vehicle and the driving distance over a certain period of time may be determined from the speed information. The driving speed variation of the simulated vehicle may be determined from the acceleration information, and the driving direction variation of the simulated vehicle, the driving distance in different driving directions, and the variation between different lanes may also be determined from the steering wheel information.

And 204, determining the position deviation between the simulated vehicle posture data and the reference vehicle posture data, and determining target traffic data corresponding to the simulated vehicle posture data from the reference traffic data according to the position deviation and the corresponding relation between the reference traffic data and the reference vehicle posture data.

Optionally, the location deviation is used to indicate a difference in geographic location of the simulated vehicle and the reference vehicle at the same point in time on the time axis. For example, assuming that the geographic position of the simulated vehicle when the simulated vehicle travels to the 10 th minute is the first geographic position and the geographic position of the reference vehicle when the reference vehicle travels to the 10 th minute is the second geographic position, it may be determined that the positional deviation of the simulated vehicle from the reference vehicle at the time point of the 10 th minute is the difference between the first geographic position and the second geographic position. And determining the difference between the first geographical position and the second geographical position as the distance difference between the first distance and the second example, assuming that the first geographical position has a first distance from the starting point and the second geographical position has a second distance from the starting point.

After determining the position deviation between the simulated vehicle and the reference vehicle, reference traffic data (referred to as "target traffic data") of the reference vehicle at a position corresponding to the first geographical position may be determined from the reference traffic data according to the position deviation, the target traffic data being indicative of road traffic conditions of the reference vehicle at the position.

In some embodiments, the reference traffic data includes position data of the road participant vehicle, that is, the target traffic data includes position data of the road participant vehicle when the reference vehicle is driven to the first position, such as: when the reference vehicle is driven to the first position, the position of the participating vehicle on the peripheral side of the vehicle is referenced.

Illustratively, when the simulated vehicle drives to the mth minute, the current position A of the simulated vehicle is determined, m is a positive number, the current position B of the reference vehicle when the simulated vehicle drives to the mth minute is determined, the time when the reference vehicle is at the position A is determined according to the position deviation between the position A and the position B, and therefore the corresponding target traffic data at the time is determined. In some embodiments, it is further required to determine the target lane data in the reference lane data corresponding to the target traffic data, that is, the lane information of the position where the reference vehicle is located at the time.

And according to the deviation of the positions of the simulated vehicle and the reference vehicle, retrieving the cached reference traffic data by combining the lane where the simulated vehicle is located and the relative distance between the simulated vehicle and the reference vehicle, and determining the target traffic data.

And step 205, planning the driving track of the simulated vehicle according to the reference lane data and the target traffic data to obtain simulated driving closed loop simulation data.

Optionally, planning a driving track of the simulated vehicle by combining the reference lane data and the position data of the participated vehicle in the target traffic data, so as to obtain simulated driving closed loop simulation data, wherein the planning process is performed in real time according to the current position of the simulated vehicle, that is, the simulated vehicle modifies the vehicle posture according to the planned driving track, so as to continue planning according to the modified vehicle posture until the simulated vehicle completes the automatic driving process, so as to obtain the final simulated driving closed loop simulation data.

Optionally, in the process of planning the driving track, according to the reference lane data and the position data of the road participating vehicle in the reference traffic data, the intended driving track of the road participating vehicle is predicted to obtain driving track prediction data of the participating vehicle, the driving track of the simulated vehicle is planned through the reference lane data and the driving track prediction data of the participating vehicle to obtain driving track planning data, and the simulated driving closed-loop simulation data is obtained according to the driving track planning data, wherein the driving plan of the simulated vehicle further includes behavior planning, and the behavior planning can obtain the planning of the driving track to a certain extent.

The driving track is planned according to the reference lane data, the simulated vehicle posture data (which can be used for determining the relationship between the simulated vehicle and the lane) and the relationship between the simulated vehicle and the lane, so that at least unexpected driving events such as the simulated vehicle pressing the lane solid line or the simulated vehicle between the two lanes for a long time can be avoided. In some embodiments, the driving track is planned according to the reference lane data, and an obstacle on a lane can be identified, and whether to control the simulated vehicle to avoid the obstacle to run is judged according to parameters such as the size and the shape of the obstacle. Such as: when the barrier is large, controlling the simulated vehicle to avoid the barrier form; or when the obstacle is smaller but has a sharp pointed end, the simulated vehicle is controlled to avoid the obstacle.

In some embodiments, feedback data simulating the return of the vehicle may be obtained and control commands generated based on the feedback data and the driving trajectory planning data. The feedback data is indicative of simulated driving conditions of the simulated vehicle, such as: and simulating the driving direction and the driving distance of the vehicle according to the latest control command. The simulated vehicle is operated according to the generated control command, and the simulated vehicle attitude data is updated according to the operation result of the simulated vehicle. And repeatedly executing the step of determining the position deviation between the simulated vehicle attitude data and the reference vehicle attitude data according to the updated simulated vehicle attitude data, and determining target traffic data corresponding to the simulated vehicle attitude data from the reference traffic data according to the position deviation and the corresponding relation between the reference traffic data and the reference vehicle attitude data to obtain the simulated driving closed loop simulation data.

The simulated driving closed-loop simulation data is used for indicating that the simulated driving process is performed in a closed loop, and indicating road information and traffic information in the closed-loop driving process of the simulated vehicle according to a reference driving data set generated by the reference vehicle, so as to assist the vehicle control of the reference vehicle in the closed-loop simulation process. In the whole driving process of the simulated vehicle, driving track planning and control are carried out on the driving of the simulated vehicle according to the lanes and the traffic conditions in the reference driving data, so that the whole simulated driving/driving process is completed, and the efficiency of the simulated driving test of the simulated driving platform is determined according to the vehicle control conditions in the simulated driving/driving process.

The simulation of the driving closed loop refers to planning and controlling the driving track of the simulated vehicle according to reference driving data from the real driving process and position feedback of the simulated vehicle in the simulation driving process. In other words, the process of making decisions on the driving trajectory planning and control of the simulated vehicle by the simulated driving closed-loop simulation platform based on the reference driving data from the actual driving process does not involve adjusting and correcting the position of the simulated vehicle based on the vehicle position corresponding to the actual driving process.

In summary, the method for simulating automatic driving provided by this embodiment performs closed-loop simulation on the automatic driving process of the simulated vehicle based on the reference lane data, the reference traffic data and the reference vehicle posture data in the reference driving data set, determines the driving trajectory plan of the simulated vehicle according to the reference lane data and the reference traffic data in real time, avoids the problem that in the open-loop simulation driving test process, because the real complete driving process of the simulated driving can not be obtained because the simulated driving condition is adjusted in real time according to the reference driving condition, thereby the condition of accurate simulation driving result can not be obtained, the testing efficiency of the automatic driving algorithm is improved, for example, at least the accuracy of the test results of the autopilot algorithm may be improved, the time required to complete the test may be reduced, or the amount of test may be reduced, etc., thereby improving the overall reliability and safety of the autopilot system.

Schematically, fig. 3 is a block diagram of a driving simulation system according to an exemplary embodiment of the present application, and as shown in fig. 3, the system includes: a packet list 310, a playback packaging unit 320, a prediction unit 330, a planning unit 340, a control unit 350, and a vehicle model 360;

wherein, the playback packaging unit 320 may include:

1) the frame information buffer module 321 is configured to dynamically adjust a data reading rate according to the overall simulation progress. In the closed loop simulation of the simulated vehicle, the playback packaging unit 320 continuously reads data from the packet list 310, and when the driving speed is fast, the rate of reading data may be increased, whereas when the driving speed is slow, the rate of reading data may be decreased. The data corresponding to each frame of image read from the data packet may be buffered, and may include static information such as lane information and vehicle information, and may also include dynamic information such as traffic information and key information of the vehicle. It should be understood, however, that the frame information buffering module is not necessary for implementation of the embodiments.

2) And a vehicle posture offset management module 322, configured to generate a deviation of the vehicle posture by using the simulated vehicle posture obtained in the closed-loop simulation process and the cached reference vehicle posture, where the simulated vehicle posture is used to represent a vehicle posture condition of the simulated vehicle, and the reference vehicle posture is used to represent a vehicle posture condition of the reference vehicle.

3) The frame information scheduling module 323 searches the cached traffic information according to the deviation of the vehicle attitude determined by the vehicle attitude deviation management module 322, and by combining the relative distance between the simulated vehicle and the lane and the relative distance between the simulated vehicle and the reference vehicle, so as to obtain the target traffic data corresponding to the current simulated vehicle.

4) And the index evaluating module 324 is used for generating a test key index. The test index is a preset index which needs to evaluate the completion condition, and can be, for example, sudden braking, sudden acceleration, distance from the preceding vehicle when starting and stopping, vehicle shaking and the like. Whether the test case meets the normal driving requirement is judged through the indexes, so that the problems existing in the process of simulating driving are identified.

It should be noted that the functions of the frame information buffer 321, the vehicle attitude offset management 322, the frame information scheduling 323, and the index evaluation 324 can be implemented in the playback encapsulation unit 320, or can be implemented by splitting into a plurality of sub-units, which is not limited in this embodiment of the present application.

The playback packaging unit 320 interacts with other units in the simulated driving system as follows:

1. the playback packaging unit 320 sends the cached lane information, other vehicle information, and modified vehicle posture information to the prediction unit 330, the prediction unit 330 performs intention and trajectory prediction on the behavior of the road participants according to the information received in real time, that is, performs intention trajectory prediction on the road participant vehicles, and the prediction unit 330 sends the prediction result to the planning unit 340.

2. The playback packaging unit 320 sends the cached lane information and the modified vehicle posture information to the planning unit 340, and the planning unit 340 plans the behavior and the track of the simulated vehicle according to the lane information and the modified vehicle posture information and according to the prediction information acquired from the prediction unit 330. Optionally, the planning unit 340 sends the simulated planned trajectory to the control unit 350. Optionally, the planning unit 340 also feeds back the simulated planned trajectory to the playback packaging unit 320.

3. The playback packaging unit 320 sends the modified vehicle posture information to the control unit 350, and the control unit 350 receives the simulated planned trajectory sent by the planning unit 340, generates a control command according to vehicle feedback returned by the vehicle model 360 of the simulated vehicle, and sends the control command to the vehicle model 360. The vehicle feedback includes position information, speed information, acceleration information, steering wheel information, and the like of the current simulated vehicle.

4. The vehicle model 360 is controlled based on the control command transmitted from the control unit 350, simulates the operation of the vehicle, and then transmits the movement of the vehicle to the playback packaging unit 320.

It should be noted that the playback packaging unit 320, the prediction unit 330, the planning unit 340, the control unit 350, and the vehicle model 360 may be implemented as different units respectively, or may be implemented as different units in one functional module, which is not limited in the embodiment of the present application.

In some embodiments, after obtaining the simulated driving closed loop simulation data, it is further required to obtain a test key indicator from the simulated driving closed loop simulation data, fig. 4 is a flowchart of an automatic driving simulation method provided in another exemplary embodiment of the present application, for example, the method is applied to a computer device, as shown in fig. 4, the method includes:

step 401, a reference driving data set is obtained, and the reference driving data set includes a reference lane data subset, a reference traffic data subset, and a reference vehicle posture data subset.

The reference lane data in the reference lane data subset, the reference traffic data in the reference traffic data subset, and the reference vehicle posture data in the reference vehicle posture data subset have a corresponding relationship.

In some embodiments, the reference driving data set is associated with a driving video during driving, and illustratively, the driving video is a video obtained by image capturing of a driving environment during driving of the reference vehicle, and the data in the reference driving data set is data generated during driving of the reference vehicle, so that image frames in the driving video correspond to the data in the reference driving data set.

In some embodiments, a data packet list is obtained, the data packet list includes data packets corresponding to different driving time periods respectively, the data packets are arranged in a forward direction according to the driving time periods, and reference driving data sets in the data packets are read from the data packet list in sequence. In some embodiments, the data packet includes a reference driving data set corresponding to the image frame in the reference driving video, and the reference driving data set is correspondingly stored according to the image frame in the reference driving video, so that the data packet is read from the data list, and the reference driving data corresponding to the image frame is obtained from the data packet frame by frame and cached. The reference driving video is a video recorded according to the process of the reference vehicle, and the image frame arrangement sequence of the reference driving video corresponds to the generation sequence of the reference driving data in the driving process of the reference vehicle, so that the reference driving data corresponding to the image frames are sequentially acquired and cached.

Step 402, operating the simulated driving platform according to the reference lane data so that the simulated vehicle can automatically simulate driving on the simulated driving platform.

The operation of the simulated driving platform is a cyclic process, namely, in the initial stage, the simulated vehicle is operated to run on a lane corresponding to the reference lane data according to the reference lane data, and in the subsequent process, feedback data and a reference driving data set generated in the driving process are acquired in real time according to the driving condition of the simulated vehicle, so that the simulated vehicle is continuously controlled.

And step 403, acquiring simulated vehicle posture data of the simulated vehicle in real time.

The simulated vehicle posture data is used for representing the vehicle driving situation of the simulated vehicle in the simulation process of automatic driving, and includes steering wheel information, speed information, acceleration information, vehicle position information and the like of the simulated vehicle, wherein the vehicle position information is used for representing the current position of the simulated vehicle, and the vehicle position information can be represented by a distance from a starting point or coordinates in a coordinate system constructed between the starting point and an end point, and the embodiment of the application is not limited thereto. And step 404, determining the position deviation between the simulated vehicle posture data and the reference vehicle posture data, and determining target traffic data corresponding to the simulated vehicle posture data from the reference traffic data according to the position deviation and the corresponding relation between the reference traffic data and the reference vehicle posture data.

Optionally, in determining the position deviation between the simulated vehicle attitude data and the reference vehicle attitude data, the position deviation is used to indicate a difference in geographic positions of the simulated vehicle and the reference vehicle at the same point in time on the time axis.

After the position deviation between the first position and the second position is determined, corresponding target traffic data in the reference traffic data when the reference vehicle drives to the first position, namely the traffic condition of the road when the reference vehicle drives to the first position, is determined according to the position deviation.

And 405, planning the driving track of the simulated vehicle according to the reference lane data and the target traffic data to obtain simulated driving closed loop simulation data.

Optionally, the driving track of the simulated vehicle is planned by combining the reference lane data and the position data of the participated vehicle in the target traffic data, so as to obtain simulated driving closed loop simulation data, wherein the planning process is performed in real time according to the position of the simulated vehicle, that is, the simulated vehicle modifies the vehicle posture according to the planning, so as to continue planning according to the modified vehicle posture until the simulated vehicle finishes automatic driving, so as to obtain final simulated driving closed loop simulation data.

Optionally, in the planning process, according to the reference lane data and the position data of the road participating vehicle, the intended trajectory of the road participating vehicle is predicted to obtain the predicted data of the participating vehicle, the driving trajectory of the simulated vehicle is planned through the reference lane data and the predicted data of the participating vehicle to obtain trajectory planning data, and the simulated driving closed-loop simulation data is determined according to the trajectory planning data, wherein when the driving trajectory is planned according to the reference lane data, the relationship between the simulated vehicle and the lane is determined according to the simulated vehicle posture data, and the driving trajectory is planned according to the relationship between the simulated vehicle and the lane, so that the simulated vehicle is prevented from pressing a lane solid line, or the simulated vehicle is prevented from driving between the two lanes for a long time.

And 406, obtaining a test key index from the simulation driving closed loop simulation data.

The test key indexes comprise at least one of brake indexes, acceleration indexes and vehicle distance indexes. The vehicle distance index corresponds to the distance between the simulated vehicle and other simulated vehicles on the road in a specified driving stage.

Optionally, the test key indexes further include a lane deviation index, a vehicle condition index and the like, where the lane deviation index is used to represent a deviation condition between the simulated vehicle and a lane center line, and the vehicle condition index is used to represent a vehicle condition of the simulated vehicle, such as: oil mass, tire pressure, etc. In this embodiment, the test key indexes including a brake index, an acceleration index, and a vehicle distance index are taken as an example for explanation.

Optionally, in an automatic simulated driving process, a test key index is generated according to the vehicle posture condition of the vehicle, the lane condition and the traffic condition.

The braking index is used for determining whether the acceleration of the simulated vehicle during braking meets a first acceleration requirement proper for braking or not, and adverse inertia influence caused by excessive braking is avoided; the acceleration index is used for determining whether the acceleration of the simulated vehicle during acceleration meets a second acceleration requirement for proper acceleration, so that the condition that the riding discomfort (such as the feeling of pushing back) is obvious due to too fast acceleration is avoided; the inter-vehicle distance indicator is used to determine that the simulated vehicle is not too close to other vehicles during a given driving phase, such as: when the vehicle is parked, the distance between the vehicle and the front vehicle is not too short.

And 407, evaluating the test key indexes to obtain a platform evaluation result of the simulated driving platform.

In some embodiments, an index evaluation result is obtained from the key indexes of the test, and in response to the index evaluation result failing, the time positioning of the failed index evaluation result in the automatic driving process is obtained to obtain a platform evaluation result.

Wherein, the index evaluation result comprises at least one of the following index evaluations:

firstly, in response to the braking acceleration reaching a first acceleration requirement, determining that a braking index fails;

illustratively, the acceleration in response to braking is less than-6 m/s²And if so, determining that the brake index does not pass, such as: in the driving process of the simulated vehicle, when the vehicle is driven for 20 seconds at the 12 th minute, the braking acceleration is-8/s²And determining that the brake index is not passed when the simulated vehicle is driven to the 12 th minute and 20 seconds.

Secondly, in response to the acceleration reaching a second acceleration requirement, determining that the acceleration index fails;

illustratively, acceleration greater than 3m/s in response to acceleration²If so, determining that the acceleration index fails, such as: in the driving process of the simulated vehicle, when the vehicle is driven to the 13 th minute for 20 seconds, the acceleration is 6/s²Then it is determined that the acceleration index has not passed when the simulated vehicle was driven to 20 seconds at 13 minutes.

Thirdly, in response to the simulated vehicle being in the designated driving stage and the distance between the simulated vehicle and other simulated vehicles on the road being less than the distance threshold value, determining that the inter-vehicle distance index fails.

Illustratively, in response to the simulated vehicle being parked and the distance between the simulated vehicle and the front vehicle being less than 0.5 m, it is determined that the inter-vehicle distance indicator fails, such as: in the driving process of the simulated vehicle, when the simulated vehicle is driven to the 14 th minute for 20 seconds, the speed of the simulated vehicle is 0m/s, and the distance between the simulated vehicle and the front vehicle is 0.3 m, the simulated vehicle is determined to be too close to the front vehicle when the simulated vehicle is driven to the 14 th minute for 20 seconds, and the inter-vehicle distance index is not passed.

It should be noted that the values of the first acceleration requirement, the second acceleration requirement, and the distance threshold are only schematic distances, and in the actual automatic simulated driving test, the first acceleration requirement, the second acceleration requirement, and the distance threshold are determined according to the setting of a designer, or the first acceleration requirement, the second acceleration requirement, and the distance threshold are determined according to the analysis of an automatic simulated driving process. The values of the first acceleration requirement, the second acceleration requirement and the distance threshold are not limited in the embodiment of the application.

The method provided by the embodiment provides an end-to-end closed-loop simulation platform based on big data, and can quickly perform end-to-end closed-loop simulation by directly utilizing data collected by road tests.

According to the method provided by the embodiment, the simulated driving platform is a closed-loop simulation platform, so that the expansion is easily supported, and more modules are conveniently introduced. Such as: if closed-loop simulation is carried out on the lane line, the lane line module can be added into the simulation driving platform, and the interfaces of the playback packaging unit and the prediction module are modified, so that the flexibility and the adaptability of automatic driving simulation are improved.

The method provided by the embodiment is extended to support distributed operation. The data packet list can be segmented, distributed operation is performed on a plurality of machines, operation results are summarized, and operation efficiency of automatic driving simulation tests is improved.

According to the method provided by the embodiment, when the actual road driving data is played back to perform closed-loop simulation to test the algorithm improvement iteration, the data set is directly used for simulation verification, the step of converting the data set into the relevant scene for re-simulation is omitted, the problem of insufficient test coverage caused by incomplete scene selection is avoided, and the test speed and reliability are improved.

Fig. 5 is a block diagram of a simulation system for automatic driving according to an exemplary embodiment of the present application, and as shown in fig. 5, the system includes: an acquisition module 510, an operation module 520, a determination module 530, and a planning module 540;

an obtaining module 510, configured to obtain a reference driving data set, where the reference driving data set includes a reference lane data subset, a reference traffic data subset, and a reference vehicle posture data subset, where reference lane data in the reference lane data subset, reference traffic data in the reference traffic data subset, and reference vehicle posture data in the reference vehicle posture data subset have a corresponding relationship;

the operation module 520 is configured to operate a simulated driving platform according to the reference lane data, so that a simulated vehicle can automatically simulate driving on the simulated driving platform;

the obtaining module 510 is further configured to obtain simulated vehicle posture data of the simulated vehicle in real time;

a determining module 530, configured to determine a location deviation between the simulated vehicle posture data and the reference vehicle posture data, and determine target traffic data corresponding to the simulated vehicle posture data from the reference traffic data according to the location deviation and according to a correspondence between the reference traffic data and the reference vehicle posture data, where the location deviation is used to indicate a difference in geographic locations of the simulated vehicle and a reference vehicle corresponding to the reference driving data set on a road;

and the planning module 540 is configured to plan the driving trajectory of the simulated vehicle according to the reference lane data and the target traffic data to obtain simulated driving closed-loop simulation data.

In an optional embodiment, the reference traffic data includes position data of road participating vehicles;

as shown in fig. 6, the planning module 540 includes:

a prediction unit 541, configured to perform an intention trajectory prediction on the road-participating vehicle according to the reference lane data and the position data of the road-participating vehicle, so as to obtain predicted data of a participating vehicle;

the planning unit 542 is configured to plan the driving track of the simulated vehicle according to the reference lane data and the predicted data of the participating vehicles, so as to obtain track planning data;

the determining unit 543 is configured to determine the simulated driving closed-loop simulation data according to the trajectory planning data.

In an optional embodiment, the determining unit 543 is further configured to receive feedback data returned by the simulated vehicle, where the feedback data is used for indicating the simulated driving condition of the simulated vehicle; generating a control command according to the feedback data and the trajectory planning data;

the determining unit 543 is further configured to run the simulated vehicle according to the control command, and generate the updated simulated vehicle posture data according to a running result of the simulated vehicle; and according to the updated simulated vehicle attitude data, repeatedly executing the steps of determining the position deviation between the simulated vehicle attitude data and the reference vehicle attitude data and determining target traffic data corresponding to the simulated vehicle attitude data from the reference traffic data according to the position deviation and the corresponding relation between the reference traffic data and the reference vehicle attitude data to obtain the simulated driving closed loop simulation data.

In an optional embodiment, the obtaining module 510 is further configured to obtain a test key indicator from the simulated driving closed-loop simulation data;

the determining module 530 is further configured to evaluate the test key indicator to obtain a platform evaluation result of the driving simulation platform.

In an optional embodiment, the obtaining module 510 is further configured to obtain an index evaluation result from the test key index; and responding to the failure of the index evaluation result, acquiring the time positioning of the failed index evaluation result in the automatic driving process, and obtaining the platform evaluation result.

the brake index corresponds to the brake acceleration of the simulated vehicle;

In an optional embodiment, the determining module 530 is further configured to determine that the braking index fails in response to the braking acceleration reaching a first acceleration requirement;

the determining module 530, further configured to determine that the acceleration indicator fails in response to the acceleration rate reaching a second acceleration request;

the determining module 530 is further configured to determine that the inter-vehicle distance indicator fails in response to a distance between the simulated vehicle and another simulated vehicle of the road being less than a distance threshold in the designated driving stage.

In an optional embodiment, the obtaining module 510 is further configured to obtain a data packet list, where the data packet list includes data packets corresponding to different driving time periods, and the data packets are arranged according to the driving time periods in a forward direction; and sequentially reading the reference driving data set in the data packet from the data packet list.

In an optional embodiment, the reference driving data corresponding to the image frame in the reference driving video is included in the data packet;

the obtaining module 510 is further configured to sequentially read the data packets from the data list; and acquiring the reference driving data corresponding to the image frames from the data packet frame by frame and caching the reference driving data.

In summary, according to the automatic driving simulation system provided by this embodiment, the reference driving data set is obtained, the closed-loop simulation is performed on the automatic driving process of the simulated vehicle according to the reference lane data, the reference traffic data, and the reference vehicle posture data in the reference driving data set, and the driving trajectory plan of the simulated vehicle is determined in real time according to the reference lane data and the reference traffic data, so that a situation that an accurate simulated driving result cannot be obtained due to the fact that the simulated driving condition is adjusted in real time according to the reference driving condition in the open-loop simulated driving test process is avoided, the accuracy of the simulated driving test is improved, and the authenticity of the simulated driving test is improved.

It should be noted that: the automatic driving simulation system provided in the above embodiment is only exemplified by the division of the above functional modules, and in practical applications, the above functions may be distributed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the above described functions. In addition, the automatic driving simulation system provided by the above embodiment and the automatic driving simulation method embodiment belong to the same concept, and the specific implementation process thereof is described in the method embodiment, and is not described herein again.

Fig. 7 shows a schematic structural diagram of a server according to an exemplary embodiment of the present application. Specifically, the method comprises the following steps:

the server 700 includes a Central Processing Unit (CPU) 701, a system Memory 704 including a Random Access Memory (RAM) 702 and a Read Only Memory (ROM) 703, and a system bus 705 connecting the system Memory 704 and the CPU 701. The server 700 also includes a mass storage device 706 for storing an operating system 713, application programs 714, and other program modules 715.

The mass storage device 706 is connected to the central processing unit 701 through a mass storage controller (not shown) connected to the system bus 705. The mass storage device 706 and its associated computer-readable media provide non-volatile storage for the server 700. That is, the mass storage device 706 may include a computer-readable medium (not shown) such as a hard disk or Compact disk Read Only Memory (CD-ROM) drive.

Without loss of generality, computer readable media may comprise computer storage media and communication media. Computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Computer storage media includes RAM, ROM, Erasable Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), flash Memory or other solid state Memory technology, CD-ROM, Digital Versatile Disks (DVD), or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage, or other magnetic storage devices. Of course, those skilled in the art will appreciate that computer storage media is not limited to the foregoing. The system memory 704 and mass storage device 706 described above may be collectively referred to as memory.

According to various embodiments of the present application, server 700 may also operate as a remote computer connected to a network via a network, such as the Internet. That is, the server 700 may be connected to the network 712 through a network interface unit 711 connected to the system bus 705, or the network interface unit 711 may be used to connect to other types of networks or remote computer systems (not shown).

The memory further includes one or more programs, and the one or more programs are stored in the memory and configured to be executed by the CPU.

Embodiments of the present application further provide a computer device, which includes a processor and a memory, where at least one instruction, at least one program, a code set, or a set of instructions is stored in the memory, and the at least one instruction, the at least one program, the code set, or the set of instructions is loaded and executed by the processor to implement the simulation method for automatic driving provided by the above method embodiments.

Embodiments of the present application further provide a computer-readable storage medium, where at least one instruction, at least one program, a code set, or a set of instructions is stored on the computer-readable storage medium, and the at least one instruction, the at least one program, the code set, or the set of instructions is loaded and executed by a processor to implement the method for simulating autopilot provided by the above method embodiments.

Embodiments of the present application also provide a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions to cause the computer device to perform the simulation method of automatic driving as described in any of the above embodiments.

Optionally, the computer-readable storage medium may include: a Read Only Memory (ROM), a Random Access Memory (RAM), a Solid State Drive (SSD), or an optical disc. The Random Access Memory may include a resistive Random Access Memory (ReRAM) and a Dynamic Random Access Memory (DRAM). The above-mentioned serial numbers of the embodiments of the present application are merely for description and do not represent the merits of the embodiments.

It will be understood by those skilled in the art that all or part of the steps for implementing the above embodiments may be implemented by hardware, or may be implemented by a program instructing relevant hardware, where the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.

The above description is only exemplary of the present application and should not be taken as limiting, as any modification, equivalent replacement, or improvement made within the spirit and principle of the present application should be included in the protection scope of the present application.

Claims

1. A method of simulating autonomous driving, the method comprising:

2. The method of claim 1, wherein the reference traffic data includes location data of road participating vehicles;

3. The method of claim 2, wherein said determining the simulated driving closed loop simulation data from the trajectory planning data comprises:

4. The method according to any one of claims 1 to 3, wherein after obtaining the simulation data of the simulated driving closed loop, the method further comprises:

5. The method of claim 4, wherein the evaluating the test key indicator to obtain a platform evaluation result of the driving simulation platform comprises:

obtaining an index evaluation result from the test key index;

6. The method of claim 4,

the test key indexes comprise at least one of brake indexes, acceleration indexes and vehicle distance indexes;

the brake index corresponds to the brake acceleration of the simulated vehicle;

7. The method of claim 6, further comprising:

8. The method of any of claims 1 to 3, wherein the obtaining a reference driving data set comprises:

9. The method of claim 8, wherein the data packet includes the reference driving data set corresponding to an image frame in a reference driving video;

sequentially reading the data packets from the data list;

10. An automated driving simulation system, the system comprising:

11. The system of claim 10, wherein the reference traffic data includes location data of road participating vehicles;

the planning module comprises:

12. The system of claim 11, wherein the determination unit is further configured to receive feedback data returned by the simulated vehicle, the feedback data being indicative of simulated driving conditions of the simulated vehicle; generating a control command according to the feedback data and the trajectory planning data;

13. The system of any one of claims 10 to 12, wherein the obtaining module is further configured to obtain a test key indicator from the simulated driving closed loop simulation data;

14. The system of claim 13, wherein the obtaining module is further configured to obtain an index evaluation result from the test key index; and responding to the failure of the index evaluation result, acquiring the time positioning of the failed index evaluation result in the automatic driving process, and obtaining the platform evaluation result.

15. The system of claim 13,

the key indexes comprise at least one of brake indexes, acceleration indexes and vehicle distance indexes;

the brake index corresponds to the brake acceleration of the simulated vehicle;

16. The system of claim 15, wherein the determination module is further configured to determine that the braking indicator failed in response to the braking acceleration reaching a first acceleration requirement;

17. The system according to any one of claims 10 to 12, wherein the obtaining module is further configured to obtain a data packet list, the data packet list includes data packets corresponding to different driving time periods, and the data packets are arranged in a forward direction according to the driving time periods; and sequentially reading the reference driving data set in the data packet from the data packet list.

18. The system of claim 17, wherein the data packet includes the reference driving data corresponding to an image frame in a reference driving video;

19. A computer device comprising a processor and a memory, the memory having stored therein at least one instruction, at least one program, a set of codes, or a set of instructions, the at least one instruction, the at least one program, the set of codes, or the set of instructions being loaded and executed by the processor to implement a simulation method of autonomous driving as claimed in any of claims 1 to 9.

20. A computer readable storage medium having stored therein at least one instruction, at least one program, a set of codes, or a set of instructions, which is loaded and executed by a processor to implement a simulation method of autonomous driving according to any of claims 1 to 9.

21. A driving simulation platform is characterized in that the driving simulation platform comprises a processor and a controller; the processor and the controller are used to control a simulated vehicle to implement an autonomous driving simulation method according to any of claims 1 to 9.